How To Stop Acquiring Customers For Your Competitors And Grow Your Business Instead

Acquisition vs. Conversion

When 500 US-based respondents were surveyed from a panel of SMBs (small and medium-sized businesses) in 2017, 57.6% said their top priority was attracting and retaining clients [1].

But when surveyed about the effort to optimize the conversion rate of their business, only 22% were satisfied with the results [2] and spent a mere $1 converting them to the $92 spent acquiring them [3].

That means a disproportionate amount of money is being spent to get prospects to a business’s “front steps”, but not to keep them there and to keep them coming back. That’s bad news given some stats from the past such as the 2011 CEI survey showing 9 out of 10 customers began doing business with a competitor following a poor customer experience [4] and AMEX’s survey showing 78% of customers bailed on a transaction or did not make an intended purchase after a poor customer experience [5]. For ecommerce businesses, a shocking $260 billion could be recouped yearly by optimizing the checkout conversion experience alone [6].

So why aren’t SMBs as a group investing more money captivating, educating, and delighting acquired prospects to convert them to paying customers?

Vocabulary

Before we dig into it, let’s define some shared vocabulary to make sure we are on the same page.

Customer Experience

End to end process of a customer interacting with your business: discovering your business, using your service, purchasing your product, getting email from you, coming back to buy again, and more.

Acquisition optimization

Getting more people to your pitch.

Finding people with a strong need for what you are selling.

Conversion & retention optimization

Helping people who’ve discovered what you are selling understand and buy it.

Providing a significant amount of value that leads to customers repeatedly using and buying.

Why conversion & retention optimization is hard

In our experience, there are two reasons acquisition tends to be the focus:

Well understood – tools for measuring acquisition return on investment (ROI) are well developed. Big companies such as Google and Facebook have spent a significant amount of resources developing them. Think Google AdWords and Facebook Ads conversion tracking.

Accessible – blogging, running social media ads, and creating offers are easy to understand and therefore more accessible as a growth strategy to the average person.

Conversion & retention optimization on the other hand is littered with challenges:

Not well understood – the ROI of the work is hard to measure. We often hear questions such as “Will a redesign actually do anything?”, “Will anyone care we removed 1 step from the signup process?”, “Our A/B tests always come back neutral”.

Requires specialized talent – you need the trifecta of engineering, design, and product sense to try ideas. Hiring a team to do this can be a very expensive and risky upfront investment to make and is typically not accessible to a resource constrained business.

What to optimize is unclear – tough to know where to start and whether the change is actually the best use of money and time.

Let’s explore each challenge below.

1. The ROI of work is not well understood

Measuring ROI of customer experience work is hard. What used to be a physical storefront or interaction with a skilled salesman explaining the ins and outs of a product on the phone has been replaced with buttons, pictures, forms, videos, and pages on a screen.

On the flip side, for this same reason, a digital product can reach millions of users and every minor interaction can be perfected. Never before have we had access to this much information about how millions of users respond to different experiences.

There are 2 commonly used techniques to measure value:

A/B testing – the practice of showing 2 different versions of an experience and then seeing which one does better.

Cohort analysis – comparing the experience of users who start with version A of the product versus users who start with version B of the product later in time.

Here’s a breakdown of the differences:

Methodology

A/B testing

Cohort analysis

Confidence

Causation, whatever you changed caused the change

Correlation, it could be that the experience is better, but it could also be other factors such as seasonality

Size needed

You need thousands of users

Works for any size

Difficulty

Hard

Requires clean, correct, and comprehensive data. The actual process has been made much easier by tools like Optimizely and Google Optimize.

Medium

Requires one clean metric such as revenue and a report to visualize it over time

Simple to understand because you simply watch to see how a certain metric changes

Sensitivity

If you change one part of the experience, don’t expect to observe the impact on Revenue right away. You need 10s of positive changes to affect a top level metric like Revenue or Conversion.

You can aggregate as many changes to compare the difference as you want.

In summary, A/B testing is extremely rigorous but requires a lot of expertise to perform. Cohort analyses are simple to perform and setup but don’t give you nearly the amount of insight or confidence about the changes you make.

Our recommendation is, if you have enough users, use both analyses in tandem to get a comprehensive understanding of how the investments you are making affect your business. If you don’t, stick to cohort analyses to start.

Dividing value by time spent to get ROI:

To compare the ROI of different changes, track how many weeks changes take and divide your key metric (such as revenue growth) by it. If your team works in shorter time frames, you can use tools like RescueTime and Toggl to get more accurate estimates. This will give you a good sense of which changes your customers are most reactive to.

It’s true a fully scaled optimization team can be very expensive, anywhere between $200k to $1M a year. The key thing is to not scale prematurely. In places where development teams are a fully funded investment, it’s not uncommon that the ROI of the team is in the range of 2-50x depending on the maturity of the product.

If you are just getting started, find someone in your company passionate about the user experience and customer service. Give them tools to perform rapid research with surveys, in person interviews, and let them try ideas with visual design editors so they can quickly experiment with changes on small percentages of the userbase. Focus on growing the ROI of their role to a 2-5x range but also ask them to rigorously document their journey so the next person can pick up where they left off.

Once you consistently see them making returns, you can help them do more by pairing them with an engineering counterpart. They’ll still be constrained by how much they can get done but now the variety of what they can do will grow. The ROI of the team will take a hit so give them some time to grow it back to the 2-5x range.

As the team grows up, you can officially promote the original experimenter to the role of a PM, balance out the team with a good ratio of engineers and designers, and continue watching and learning about your market. At some point, this team will likely plateau. This is actually good news and means you are ready for the next major step and coincidentally problem #3.

3. Knowing what to optimize is unclear

It’s common for product teams to encounter plateaus when optimizing a product, plateaus which take a significant amount of research and trial-and-error to overcome. If you are at this stage, the good news is your product is fairly well optimized. The bad news is you’re out of easy changes. Luckily, we’ve recently seen an emergence of a plateau breaking role, the data scientist.

Data science done right helps the team learn from existing users significantly faster and also helps qualitative research target the right customers to learn from. By observing how masses of people act inside your product and how they have responded to all the changes you have made up to this point, you can find patterns of how and why users use your product. By segmenting user types and then observing their experience through time, you can start getting a macro level understanding of everything happening in your product. This is how plateaus can repeatedly be broken. The optimization team can then march on.

(NOTE: We built Asgard to act as a companion data scientist for teams at every stage of growth. Email us if you are interested in working with us.)

Conclusion

In conclusion, there’s probably a big opportunity to grow your revenue and business when it comes to conversion & retention optimization. There are definitely some obstacles but given that more than two-thirds of marketers say their companies compete mostly on the basis of customer experience, according to the 2017 Gartner Customer Experience in Marketing Survey and in two years’ time, 81% say they expect to be competing mostly or completely on the basis of CX, now is definitely a good time to start developing your team [7].